What this category of data looks like
Data for this category comes from state-owned assets supervision and administration commission investment supervision systems, enterprise ERP and project management systems, industry market data, and audit financial statements. Commonly involved documents include annual investment plans, project proposals, feasibility study reports, due diligence reports, asset appraisal reports, and post-investment evaluation reports. Data is updated quarterly. Fields cover project approval numbers, investment amounts, implementation cycles, post-investment performance indicators, and more. Some documents are semi-structured review comments and meeting minutes. Ledger data requires cross-domain field association across multiple systems.
What constraints these characteristics impose on model access and configuration
Quarterly updated data means model calls do not need high-frequency triggering. Full ledger data can be processed in batches each quarter. Cross-domain fields from multiple sources require models to support multi-document context association, to avoid fragmented data. Mixed input of semi-structured review materials and structured ledgers requires model configurations to balance long-text understanding and structured field extraction. Post-investment evaluation involves content related to state-owned assets. Model calls must ensure data compliance, and configurations must limit unauthorized data leakage. When there are a large number of projects, the stability of batch processing affects the overall workflow. Context and call parameters must be set appropriately.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Documents in this category often combine multiple feasibility study reports and review materials, requiring space for cross-system associated field context |
maxTokens | 4000–6000 token | Ledger summary and report generation require output of structured tables and analytical text, requiring a balance between output length and content completeness |
temperature | 0.3–0.5 | Post-investment evaluation and project approval reports require content rigor, to avoid irrelevant, divergent generation |
AIPROXY_API_ENDPOINT | Fill in according to the official endpoint of the accessed model | Ensure correct model request routing, adapt to access specifications for models from different vendors |
AIPROXY_API_TOKEN | Fill in according to the key assigned by the platform | Complete identity verification for model calls, ensuring interface access security |
quoteMaxToken | 1500–2000 token | Valid reference fragments must be extracted from multiple industry reports and financial statements, to avoid truncation of critical data |
Three common mistakes
- The issue is that the batch execution node completes the full workflow during online debugging, but returns partial task failures or incomplete status codes via API calls. The cause is that the API call does not carry context parameters for batch loops, while online debugging retains intermediate variables from session cache.
- The issue is that calling a vision model returns format errors or no visual analysis results. The cause is that the
visionparameter is not enabled in the configuration, or the model's visual inference endpoint is not configured correctly. - The issue is that the application's token consumption statistics data is empty or does not match actual calls. The cause is that token tracking configuration for model calls is not enabled, or the statistics logic is not linked to the batch tasks for ledger summary and report generation in this category.
How to confirm the configuration is correct
- Call the test interface, input a single project proposal and ledger data, verify that the output summary includes correct cross-system field associations.
- Check the model call logs, confirm that the actual usage values of
maxContextandmaxTokensmatch the configured values. - Review the token consumption statistics page, confirm that consumption data for the corresponding batch has been generated.
- Initiate a batch API call, verify that the number of returned results matches the number of pending projects.
The values provided are common starting points and should be measured against the user's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.